An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information

Citations Over TimeTop 10% of 2016 papers

Abstract

Lack of human resources and radiological interpretation expertise impair tuberculosis (TB) screening programmes in TB-endemic countries. Computer-aided detection (CAD) constitutes a viable alternative for chest radiograph (CXR) reading. However, no automated techniques that exploit the additional clinical information typically available during screening exist. To address this issue and optimally exploit this information, a machine learning-based combination framework is introduced. We have evaluated this framework on a database containing 392 patient records from suspected TB subjects prospectively recruited in Cape Town, South Africa. Each record comprised a CAD score, automatically computed from a CXR, and 12 clinical features. Comparisons with strategies relying on either CAD scores or clinical information alone were performed. Our results indicate that the combination framework outperforms the individual strategies in terms of the area under the receiving operating characteristic curve (0.84 versus 0.78 and 0.72), specificity at 95% sensitivity (49% versus 24% and 31%) and negative predictive value (98% versus 95% and 96%). Thus, it is believed that combining CAD and clinical information to estimate the risk of active disease is a promising tool for TB screening.

Related Papers

• → Clinical, laboratory, and radiological information in the diagnosis of pneumonia in children(1988)76 cited
• [Application of computer aided diagnosis (CAD) in clinical imaging. Presentation of the value and current directions in CAD development in various imaging techniques based on literature review].(2009)
• → Computer‐Assisted Detection and Diagnosis(2006)2 cited
• → Computer‐Aided Diagnosis (CAD) and Image‐Guided Decision Support(2009)
• → Computer-aided detection for CT colonography(2014)